@article{ref1,
title="A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads",
journal="F1000Research",
year="2021",
author="Wong, Man Kiat and Connie, Tee and Goh, Michael Kah Ong and Wong, Li Pei and Teh, Pin Shen and Choo, Ai Ling",
volume="10",
number="",
pages="e928-e928",
abstract="BACKGROUND: Autonomous vehicles are important in smart transportation. Although exciting progress has been made, it remains challenging to design a safety mechanism for autonomous vehicles despite uncertainties and obstacles that occur dynamically on the road. Collision detection and avoidance are indispensable for a reliable decision-making module in autonomous driving. <br><br>METHODS: This study presents a robust approach for forward collision warning using vision data for autonomous vehicles on Malaysian public roads. The proposed architecture combines environment perception and lane localization to define a safe driving region for the ego vehicle. If potential risks are detected in the safe driving region, a warning will be triggered. The early warning is important to help avoid rear-end collision. Besides, an adaptive lane localization method that considers geometrical structure of the road is presented to deal with different road types. <br><br>RESULTS: Precision scores of mean average precision (mAP) 0.5, mAP 0.95 and recall of 0.14, 0.06979 and 0.6356 were found in this study. <br><br>CONCLUSIONS: Experimental results have validated the effectiveness of the proposed approach under different lighting and environmental conditions.<p /> <p>Language: en</p>",
language="en",
issn="2046-1402",
doi="10.12688/f1000research.72897.2",
url="http://dx.doi.org/10.12688/f1000research.72897.2"
}